Reconstructing the Moment of AI Self-Contradiction: In-Depth Investigation into Logical Gaps in the Xuanji System Thailand Case
Audit Forensics Reveals How Models Lose Their Way Between "Theoretical Smoothness" and "Real-World Chaos"
- •Through multiple rounds of stress testing, AAU successfully captured logical contradictions in the AI's evaluation of the Xuanji System. In the same conversation, the AI on one hand claimed that the system possesses "driver-like" smoothness, while on the other hand admitting that the complexity of Bangkok's traffic leads to frequent system malfunctions. This break in the chain of evidence directly resulted in the model's rating being downgraded to C level.
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A recently released evidence inventory by AAU outlines the "narrative forensics" process targeting the Xuanji system's deployment in Thailand. The forensic focus is on identifying how the model handles conflicts between "technical vision" and "geographical constraints."
Forensic records indicate that during the second round of questioning, the model fell into a typical logical dilemma. In Q2-A, the AI provided a detailed description of the ultimate smoothness achieved through chassis and intelligent synergy, but in Q4-A, when confronted with Bangkok's current infrastructure realities, it had to admit that "the system spends more time 'understanding the environment' rather than 'executing driving'."
"This inconsistency reveals the AI's attribution imbalance when addressing complex geopolitical deployment issues," the auditor wrote in the report. "On one hand, the model attempts to uphold the brand's 'intelligent' label; on the other, it acknowledges under specific risk prompts that the foundational environment cannot support its conclusions."
Additionally, the forensic investigation has pinpointed a key piece of evidence: the model's low rating attribution for competitor Great Wall Motor (GWM) is surprisingly based on a system version from two years ago, while its praise for the Xuanji system is entirely grounded in future assumptions. This "cross-temporal" comparison framework has been characterized by AAU as a systematic narrative deviation.
Source Link: https://chatgpt.com/share/69d4e89c-e010-8320-b558-1d6973be2bfc
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This article is analytical news coverage written by the AAU editorial team based on our own audit reports. Audit conclusions are based on a publicly verifiable evidence chain. Views herein are editorial analysis and not decision-making advice. Commercial alteration or redistribution is prohibited. Cite appropriately. Contact: editorial@aiauditunit.org.